from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 39.0 | 56.815357 |
| daal4py_KNeighborsClassifier | 0.0 | 2.0 | 32.864518 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 27.430972 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 27.479858 |
| KMeans_tall | 0.0 | 0.0 | 21.353145 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 7.817818 |
| KMeans_short | 0.0 | 0.0 | 2.871868 |
| daal4py_KMeans_short | 0.0 | 0.0 | 1.389684 |
| LogisticRegression | 0.0 | 0.0 | 18.777409 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 3.860825 |
| Ridge | 0.0 | 0.0 | 10.258869 |
| daal4py_Ridge | 0.0 | 0.0 | 1.876589 |
| HistGradientBoostingClassifier | 0.0 | 5.0 | 0.255292 |
| lightgbm | 0.0 | 5.0 | 7.348520 |
| xgboost | 0.0 | 7.0 | 57.898713 |
| catboost | 0.0 | 5.0 | 10.135635 |
| total | 1.0 | 9.0 | 48.552901 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.477 | 0.000 | 1.677 | 0.000 | -1 | 100 | NaN | NaN | 0.458 | 0.000 | 1.042 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 32.557 | 0.000 | 0.000 | 0.033 | -1 | 100 | 0.940 | 0.736 | 1.810 | 0.043 | 17.987 | 0.423 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.161 | 0.011 | 0.000 | 0.161 | -1 | 100 | 0.000 | 1.000 | 0.080 | 0.001 | 2.025 | 0.134 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.121 | 0.000 | 6.635 | 0.000 | -1 | 5 | NaN | NaN | 0.441 | 0.000 | 0.273 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 32.232 | 0.000 | 0.000 | 0.032 | -1 | 5 | 0.806 | 0.828 | 1.797 | 0.019 | 17.937 | 0.194 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.163 | 0.013 | 0.000 | 0.163 | -1 | 5 | 1.000 | 1.000 | 0.078 | 0.001 | 2.081 | 0.173 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.121 | 0.000 | 6.612 | 0.000 | 1 | 100 | NaN | NaN | 0.441 | 0.000 | 0.274 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 21.065 | 0.090 | 0.000 | 0.021 | 1 | 100 | 0.940 | 0.934 | 1.886 | 0.047 | 11.167 | 0.280 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.182 | 0.000 | 0.000 | 0.182 | 1 | 100 | 0.000 | 1.000 | 0.084 | 0.010 | 2.166 | 0.265 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.119 | 0.000 | 6.749 | 0.000 | 1 | 1 | NaN | NaN | 0.444 | 0.000 | 0.267 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 11.987 | 0.038 | 0.000 | 0.012 | 1 | 1 | 0.722 | 0.736 | 1.800 | 0.022 | 6.660 | 0.082 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.170 | 0.001 | 0.000 | 0.170 | 1 | 1 | 1.000 | 1.000 | 0.080 | 0.001 | 2.130 | 0.030 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.117 | 0.000 | 6.854 | 0.000 | -1 | 1 | NaN | NaN | 0.443 | 0.000 | 0.264 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 22.888 | 0.167 | 0.000 | 0.023 | -1 | 1 | 0.722 | 0.934 | 1.847 | 0.028 | 12.392 | 0.207 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.155 | 0.018 | 0.000 | 0.155 | -1 | 1 | 1.000 | 1.000 | 0.079 | 0.001 | 1.967 | 0.226 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.117 | 0.000 | 6.845 | 0.000 | 1 | 5 | NaN | NaN | 0.439 | 0.000 | 0.266 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 20.836 | 0.048 | 0.000 | 0.021 | 1 | 5 | 0.806 | 0.828 | 1.855 | 0.047 | 11.234 | 0.283 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.181 | 0.002 | 0.000 | 0.181 | 1 | 5 | 1.000 | 1.000 | 0.084 | 0.008 | 2.162 | 0.197 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.055 | 0.000 | 0.293 | 0.000 | -1 | 100 | NaN | NaN | 0.099 | 0.000 | 0.549 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 29.473 | 0.067 | 0.000 | 0.029 | -1 | 100 | 0.986 | 0.979 | 0.289 | 0.007 | 102.110 | 2.476 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.001 | 0.000 | 0.021 | -1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 3.632 | 0.224 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.288 | 0.000 | -1 | 5 | NaN | NaN | 0.101 | 0.000 | 0.551 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 29.471 | 0.082 | 0.000 | 0.029 | -1 | 5 | 0.983 | 0.992 | 0.291 | 0.004 | 101.240 | 1.555 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.003 | 0.000 | 0.021 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 3.884 | 0.509 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.300 | 0.000 | 1 | 100 | NaN | NaN | 0.103 | 0.000 | 0.520 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 18.837 | 0.040 | 0.000 | 0.019 | 1 | 100 | 0.986 | 0.988 | 0.330 | 0.008 | 57.138 | 1.332 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.017 | 0.000 | 0.000 | 0.017 | 1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 3.064 | 0.146 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.294 | 0.000 | 1 | 1 | NaN | NaN | 0.099 | 0.000 | 0.552 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 9.342 | 0.019 | 0.000 | 0.009 | 1 | 1 | 0.974 | 0.979 | 0.277 | 0.004 | 33.699 | 0.442 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.014 | 0.000 | 0.000 | 0.014 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 2.538 | 0.162 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.298 | 0.000 | -1 | 1 | NaN | NaN | 0.098 | 0.000 | 0.546 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.177 | 0.114 | 0.000 | 0.020 | -1 | 1 | 0.974 | 0.988 | 0.331 | 0.006 | 60.875 | 1.073 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.018 | 0.003 | 0.000 | 0.018 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 3.368 | 0.493 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.298 | 0.000 | 1 | 5 | NaN | NaN | 0.099 | 0.000 | 0.542 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 18.750 | 0.029 | 0.000 | 0.019 | 1 | 5 | 0.983 | 0.992 | 0.274 | 0.006 | 68.415 | 1.536 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.016 | 0.000 | 0.000 | 0.016 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 3.232 | 0.187 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.046 | 0.000 | 0.026 | 0.000 | 1 | 100 | NaN | NaN | 0.696 | 0.000 | 4.379 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.368 | 0.038 | 0.000 | 0.004 | 1 | 100 | 0.977 | 0.981 | 0.560 | 0.007 | 7.800 | 0.124 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | 1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 4.552 | 2.076 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.022 | 0.000 | 0.026 | 0.000 | -1 | 5 | NaN | NaN | 0.705 | 0.000 | 4.286 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.759 | 0.013 | 0.000 | 0.001 | -1 | 5 | 0.978 | 0.977 | 0.180 | 0.004 | 4.215 | 0.111 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 8.845 | 3.437 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.999 | 0.000 | 0.027 | 0.000 | 1 | 1 | NaN | NaN | 0.677 | 0.000 | 4.434 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.654 | 0.006 | 0.000 | 0.001 | 1 | 1 | 0.969 | 0.963 | 0.097 | 0.002 | 6.769 | 0.127 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 4.915 | 2.438 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.985 | 0.000 | 0.027 | 0.000 | 1 | 5 | NaN | NaN | 0.674 | 0.000 | 4.427 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.289 | 0.019 | 0.000 | 0.001 | 1 | 5 | 0.978 | 0.981 | 0.549 | 0.007 | 2.349 | 0.047 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 1.852 | 0.789 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.069 | 0.000 | 0.026 | 0.000 | -1 | 100 | NaN | NaN | 0.685 | 0.000 | 4.482 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.537 | 0.017 | 0.000 | 0.003 | -1 | 100 | 0.977 | 0.977 | 0.181 | 0.003 | 13.991 | 0.238 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.012 | 0.019 | 0.000 | 0.012 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 34.993 | 56.103 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.849 | 0.000 | 0.028 | 0.000 | -1 | 1 | NaN | NaN | 0.694 | 0.000 | 4.108 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.400 | 0.005 | 0.000 | 0.000 | -1 | 1 | 0.969 | 0.963 | 0.099 | 0.003 | 4.064 | 0.138 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 11.173 | 5.486 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.767 | 0.000 | 0.021 | 0.000 | 1 | 100 | NaN | NaN | 0.465 | 0.000 | 1.650 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.053 | 0.000 | 0.000 | 0.000 | 1 | 100 | 0.984 | 0.980 | 0.006 | 0.001 | 8.214 | 0.759 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.060 | 3.555 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.761 | 0.000 | 0.021 | 0.000 | -1 | 5 | NaN | NaN | 0.451 | 0.000 | 1.688 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.984 | 0.981 | 0.001 | 0.000 | 24.785 | 6.744 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 18.394 | 13.148 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.742 | 0.000 | 0.022 | 0.000 | 1 | 1 | NaN | NaN | 0.462 | 0.000 | 1.605 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.023 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.977 | 0.974 | 0.001 | 0.000 | 32.163 | 10.046 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.355 | 3.878 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.773 | 0.000 | 0.021 | 0.000 | 1 | 5 | NaN | NaN | 0.459 | 0.000 | 1.684 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.984 | 0.980 | 0.006 | 0.000 | 4.105 | 0.257 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.804 | 3.777 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.812 | 0.000 | 0.020 | 0.000 | -1 | 100 | NaN | NaN | 0.453 | 0.000 | 1.795 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.045 | 0.004 | 0.000 | 0.000 | -1 | 100 | 0.984 | 0.981 | 0.001 | 0.000 | 41.695 | 10.852 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 19.033 | 13.150 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.773 | 0.000 | 0.021 | 0.000 | -1 | 1 | NaN | NaN | 0.456 | 0.000 | 1.698 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.025 | 0.000 | 0.001 | 0.000 | -1 | 1 | 0.977 | 0.974 | 0.001 | 0.000 | 35.325 | 9.483 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 19.310 | 13.729 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.613 | 0.0 | 0.783 | 0.000 | k-means++ | NaN | 30 | NaN | 0.408 | 0.0 | 1.503 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.383 | 0.000 | k-means++ | 0.001 | 30 | 0.000 | 0.000 | 0.0 | 8.037 | 4.300 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.714 | 7.606 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.479 | 0.0 | 1.002 | 0.000 | random | NaN | 30 | NaN | 0.431 | 0.0 | 1.112 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.387 | 0.000 | random | 0.001 | 30 | 0.000 | 0.000 | 0.0 | 8.172 | 4.892 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.403 | 8.190 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.175 | 0.0 | 3.887 | 0.000 | k-means++ | NaN | 30 | NaN | 2.593 | 0.0 | 2.382 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.792 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.725 | 2.402 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.148 | 5.344 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 5.665 | 0.0 | 4.236 | 0.000 | random | NaN | 30 | NaN | 2.773 | 0.0 | 2.043 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.096 | 0.000 | random | 0.001 | 30 | 0.002 | 0.000 | 0.0 | 5.680 | 2.562 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.798 | 4.787 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.235 | 0.0 | 0.014 | 0.000 | k-means++ | NaN | 20 | NaN | 0.034 | 0.0 | 6.923 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.189 | 0.000 | k-means++ | 0.001 | 20 | 0.002 | 0.001 | 0.0 | 2.609 | 0.488 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 10.375 | 7.157 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.080 | 0.0 | 0.040 | 0.000 | random | NaN | 20 | NaN | 0.087 | 0.0 | 0.921 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.189 | 0.000 | random | -0.000 | 20 | 0.001 | 0.001 | 0.0 | 2.535 | 0.445 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.919 | 6.370 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.641 | 0.0 | 0.250 | 0.000 | k-means++ | NaN | 20 | NaN | 0.137 | 0.0 | 4.693 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.531 | 0.000 | k-means++ | 0.316 | 20 | 0.269 | 0.001 | 0.0 | 2.079 | 0.280 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.022 | 4.298 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.212 | 0.0 | 0.756 | 0.000 | random | NaN | 20 | NaN | 0.331 | 0.0 | 0.640 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.548 | 0.000 | random | 0.322 | 20 | 0.311 | 0.001 | 0.0 | 2.208 | 0.323 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.605 | 3.593 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 10.424 | 0.0 | [-0.11318398] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.767 | 0.000 | 5.898 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [51.41401393] | 0.000 | NaN | NaN | NaN | NaN | 0.536 | 0.000 | 0.000 | 0.924 | 0.426 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.20775902] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.465 | 0.419 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [27] | 0.766 | 0.0 | [-2.78891705] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.746 | 0.000 | 1.026 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [27] | 0.002 | 0.0 | [133.37056183] | 0.000 | NaN | NaN | NaN | NaN | 0.230 | 0.003 | 0.001 | 0.521 | 0.245 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [27] | 0.000 | 0.0 | [22.70510277] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.000 | 0.136 | 0.086 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.183 | 0.0 | 0.437 | 0.0 | NaN | NaN | NaN | 0.177 | 0.000 | 1.038 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.0 | 8.132 | 0.0 | NaN | NaN | 0.122 | 0.017 | 0.002 | 0.575 | 0.064 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 1.013 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.726 | 0.678 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.325 | 0.0 | 0.604 | 0.0 | NaN | NaN | NaN | 0.226 | 0.000 | 5.855 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 3.590 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.980 | 1.414 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.014 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.675 | 0.758 | See | See |
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
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}
],
"cpu_count": 2
}